Ali H. Sayed

Bio: Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.

Decision-Making Algorithms for Learning and Adaptation with Application to COVID-19 Data.

Abstract: This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for decision problems. We propose a new scheme, referred to as BLLR (barrier log-likelihood ratio algorithm) and demonstrate its applicability to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak.

Distributed primal strategies outperform primal-dual strategies over adaptive networks

Abstract: This work studies distributed primal-dual strategies for adaptation and learning over networks from streaming data. Two first-order methods are considered based on the Arrow-Hurwicz (AH) and augmented Lagrangian (AL) techniques. Several results are revealed in relation to the performance and stability of these strategies when employed over adaptive networks. It is found that these methods have worse steady-state mean-square-error performance than primal methods of the consensus and diffusion type. It is also found that the AH technique can become unstable under a partial observation model, while the other techniques are able to recover the unknown under this scenario. It is further shown that AL techniques are stable over a narrower range of step-sizes than primal strategies.

Affine Projection Algorithm

Abstract: This chapter contains sections titled: Instantaneous Approximation Computational Cost Least-Perturbation Property Affine Projection Interpretation

Enhanced Diffusion Learning Over Networks

Abstract: This work develops a variation of diffusion learning by incorporating an adaptive construction for the combination weights through local fusion steps. This leads to an implementation with enhanced convergence rate and mean-square-error performance while maintaining the same level of complexity as standard implementations. The approach is based on formulating optimal or close-to-optimal learning and fusion steps using a proximity function rationale within neighborhoods. The first version of the algorithm employs exact fusion in the least-squares sense using inverses of uncertainty matrices. The second version replaces these matrices by diagonal approximations with reduced complexity. The result is an LMS-complexity scheme with improved performance for distributed learning over networks.

An adaptive filter robust to data uncertainties

Abstract: This paper considers the problem of adaptive filtering in the presence of uncertainties in the regression data. A recursive procedure is derived that is based on solving local optimization problems that attemps to alleviate the worst-case effect of data uncertainties on filter performance. The resulting procedure turns out to have similarities with leakage-based adaptive filters.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Tensor Decompositions and Applications

Abstract: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Table Of Integrals Series And Products

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